Xiaofeng Shao : Change-point detection for COVID-19 time series via self-normalization

Posted by Ping-Shou Zhong , part of the Statistics and Data Science Seminar.

At

April 28, 2021, 4 p.m.

In

Zoom

Abstract

This talk consists of two parts. In the first part, I will review some basic idea of self-normalization (SN) for inference of time series in the context of confidence interval construction and change-point testing in mean. In the second part, I will present a piecewise linear quantile trend model to model infection trajectories of COVID-19 daily new cases. To estimate the change-points in the linear trend, we develop a new segmentation algorithm based on SN test statistics and local scanning. Data analysis for COVID-19 infection trends in many countries demonstrates the usefulness of our new model and segmentation method.